{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目标检测中的坐标回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "全卷积网络，预测车牌16个关键。\n",
    "和大多数目标检测网络中的BBox定位一样，目标检测中是要预测BBox的x，y，w和h，其实思想是一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from skimage import io,transform\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import time\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name: 20171016121026584(粤B33533D-绿)sp.jpg\n",
      "Landmarks shape: (16, 2)\n",
      "First 4 Landmarks: [[ 24   1]\n",
      " [ 64   7]\n",
      " [104  15]\n",
      " [142  22]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"PlateKeyPoint/train.txt\",\"r\") as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "img_name, landmarks = lines[0].strip().split(\" \",1)\n",
    "# print(landmarks)\n",
    "landmarks = [int(l) for l in landmarks.split(\" \")]\n",
    " \n",
    "# print(landmarks)\n",
    "landmarks = np.array(landmarks)\n",
    "# print(landmarks)\n",
    "landmarks = landmarks.reshape(-1,2)\n",
    "print('Image name: {}'.format(img_name))\n",
    "# print(landmarks)\n",
    "print('Landmarks shape: {}'.format(landmarks.shape))\n",
    "print('First 4 Landmarks: {}'.format(landmarks[:4]))\n",
    " \n",
    "def show_landmarks(image, landmarks):\n",
    "    \"\"\"Show image with landmarks\"\"\"\n",
    "    plt.imshow(image)\n",
    "    plt.scatter(landmarks[:, 0], landmarks[:, 1], s=20, marker='.', c='r',linewidths = 5)\n",
    " \n",
    "plt.figure()\n",
    "show_landmarks(io.imread(os.path.join('PlateKeyPoint/train/', img_name)),\n",
    "               landmarks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 重写数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_label(label_file):\n",
    "    with open(label_file,\"r\") as f:\n",
    "        lines = f.readlines()\n",
    "    newlines = [line.strip().split(\" \",1) for line in lines]\n",
    "    return newlines\n",
    "    \n",
    "class PlateKeyPointDataset(Dataset):\n",
    "    def __init__(self, label_file, root_dir, transform = None):\n",
    "        self.keypoint = read_label(label_file)\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.keypoint)\n",
    "    \n",
    "    def __getitem__(self,idx):\n",
    "        img_name = os.path.join(self.root_dir,self.keypoint[idx][0])\n",
    "        image = io.imread(img_name)\n",
    "        kp = self.keypoint[idx][1]\n",
    "        kp = [int(p) for p in kp.split(\" \")]\n",
    "        kp = np.array(kp)\n",
    "        kp = kp.astype(\"float\").reshape(-1,2)\n",
    "        sample = image,kp\n",
    "        if self.transform:\n",
    "            sample = self.transform(sample)            \n",
    "        return sample      \n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实现简单的预处理转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Rescale(object):\n",
    "    \"\"\"Rescale the image in a sample to a given size.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If tuple, output is\n",
    "            matched to output_size. If int, smaller of image edges is matched\n",
    "            to output_size keeping aspect ratio the same.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        self.output_size = output_size\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, kp = sample[0], sample[1]\n",
    "\n",
    "        h, w = image.shape[:2]\n",
    "        if isinstance(self.output_size, int):\n",
    "            if h > w:\n",
    "                new_h, new_w = self.output_size * h / w, self.output_size\n",
    "            else:\n",
    "                new_h, new_w = self.output_size, self.output_size * w / h\n",
    "        else:\n",
    "            new_h, new_w = self.output_size\n",
    "\n",
    "        new_h, new_w = int(new_h), int(new_w)\n",
    "\n",
    "        img = transform.resize(image, (new_h, new_w))\n",
    "\n",
    "        # h and w are swapped for landmarks because for images,\n",
    "        # x and y axes are axis 1 and 0 respectively\n",
    "        kp = kp * [new_w / w, new_h / h]\n",
    "#         kp = kp.flatten()\n",
    "\n",
    "        return img, kp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ToTensor(object):\n",
    "    \"\"\"Convert ndarrays in sample to Tensors.\n",
    "        numpy数组到tensor的变化，另外还有维度的变化。\n",
    "    \"\"\"\n",
    " \n",
    "    def __call__(self, sample):\n",
    "        image, kp = sample[0], sample[1]\n",
    " \n",
    "        # swap color axis because\n",
    "        # numpy image: H x W x C\n",
    "        # torch image: C X H X W\n",
    "        image = image.transpose((2, 0, 1))\n",
    "        image = torch.from_numpy(image)\n",
    "        label = torch.from_numpy(kp)\n",
    "        return image.type(torch.FloatTensor),label.type(torch.FloatTensor),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "platekptrain = PlateKeyPointDataset(label_file=\"./PlateKeyPoint/train.txt\",root_dir=\"./PlateKeyPoint/train\",transform=transforms.Compose([Rescale((64,128)),ToTensor()]))\n",
    "platekpval = PlateKeyPointDataset(label_file=\"./PlateKeyPoint/val.txt\",root_dir=\"./PlateKeyPoint/val\",transform=transforms.Compose([Rescale((64,128)),ToTensor()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小：693\n",
      "验证集大小：41\n"
     ]
    }
   ],
   "source": [
    "print(\"训练集大小：%d\"%len(platekptrain))\n",
    "print(\"验证集大小：%d\"%len(platekpval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# platekp2 = PlateKeyPointDataset(label_file=\"./PlateKeyPoint/train.txt\",root_dir=\"./PlateKeyPoint/train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = plt.figure()\n",
    "# for i in range(len(platekp2)):\n",
    "#     sample = platekp[i]\n",
    "#     print(i, sample[0].shape, sample[].shape )\n",
    "#     ax = plt.subplot(1,4,i+1)\n",
    "#     plt.tight_layout()\n",
    "#     ax.set_title('keypoint #{}'.format(i))\n",
    "#     ax.axis('off')\n",
    "# #     print(*sample.values())\n",
    "#     show_landmarks(*sample.values())\n",
    "#     if i == 3:\n",
    "#         plt.show()\n",
    "#         break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lr衰减和warm up，使用xavier初始化CNN权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_learning_rate(optimizer, gamma, step):\n",
    "    \"\"\"Sets the learning rate to the initial LR decayed by 10 at every\n",
    "        specified step\n",
    "    # Adapted from PyTorch Imagenet example:\n",
    "    # https://github.com/pytorch/examples/blob/master/imagenet/main.py\n",
    "    \"\"\"\n",
    "    lr = initial_lr * (gamma ** (step))\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "\n",
    "def warmup_learning_rate(optimizer,iteration):\n",
    "    lr_ini = 0.000001\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr_ini+(initial_lr - lr_ini)*iteration/100\n",
    "\n",
    "def xavier(param):\n",
    "    init.xavier_uniform_(param)\n",
    "\n",
    "\n",
    "def weights_init(m):\n",
    "    if isinstance(m, nn.Conv2d):\n",
    "        xavier(m.weight.data)\n",
    "        m.bias.data.zero_()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader = DataLoader(platekptrain, batch_size=32, shuffle=True, num_workers=4)\n",
    "valloader = DataLoader(platekpval, batch_size=32, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型搭建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网络结构搭建，这这里仿照VGG结构，去掉全连接层，并在最后加入1x1卷积用于关键点的回归\n",
    "![](https://t9.baidu.com/it/u=463051614,2727077879&fm=193)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGGRegNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGGRegNet, self).__init__()\n",
    "        self.conv1_0 = nn.Conv2d(3,16,3,padding=1)\n",
    "        self.conv1_1 = nn.Conv2d(16,16,3,padding=1)\n",
    "        self.conv2_0 = nn.Conv2d(16,32,3,padding=1)\n",
    "        self.conv2_1 = nn.Conv2d(32,32,3,padding=1)\n",
    "        self.conv3_0 = nn.Conv2d(32,64,3,padding=1)\n",
    "        self.conv3_1 = nn.Conv2d(64,64,3,padding=1)\n",
    "        self.conv4_0 = nn.Conv2d(64,128,3,padding=1)\n",
    "        self.conv4_1 = nn.Conv2d(128,128,3,padding=1)\n",
    "        self.conv5_0 = nn.Conv2d(128,256,3,padding=1)\n",
    "        self.conv5_1 = nn.Conv2d(256,256,3,padding=1)\n",
    "        self.conv6_0 = nn.Conv2d(256,512,3,padding=1)\n",
    "        self.conv6_1 = nn.Conv2d(512,512,3,padding=1)\n",
    "        self.loc = nn.Conv2d(512,16,1)\n",
    "                     \n",
    "    def forward(self, x):                             #64x128x3\n",
    "        x = F.relu(self.conv1_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv1_1(x)),2)     #32x64x16\n",
    "        x = F.relu(self.conv2_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv2_1(x)),2)     #16x32x32\n",
    "        x = F.relu(self.conv3_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv3_1(x)),2)     #8x16x64\n",
    "        x = F.relu(self.conv4_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv4_1(x)),2)     #4x8x128\n",
    "        x = F.relu(self.conv5_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv5_1(x)),2)     #2x4x256\n",
    "        x = F.relu(self.conv6_0(x))\n",
    "        x = F.max_pool2d(F.relu(self.conv6_1(x)),2)     #1x2x512\n",
    "        x = self.loc(x)                                 #1*2*16\n",
    "#         x = x.view()\n",
    "        return x       \n",
    "        \n",
    "    def num_flat_features(self, x):\n",
    "        size = x.size()[1:]\n",
    "        num_features = 1\n",
    "        for i in size:\n",
    "            num_features *= i\n",
    "        return num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGGRegNet(\n",
      "  (conv1_0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv1_1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2_0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2_1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3_0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3_1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4_0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4_1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv5_0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv5_1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv6_0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv6_1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (loc): Conv2d(512, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net= VGGRegNet()\n",
    "net.apply(weights_init)\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.SmoothL1Loss()\n",
    "initial_lr = 0.001\n",
    "optimizer = optim.AdamW(net.parameters(), lr = initial_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VGGRegNet(\n",
       "  (conv1_0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv1_1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6_0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6_1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (loc): Conv2d(512, 16, kernel_size=(1, 1), stride=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "net.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网络训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10  || LR 0.000091 || TrainLoss: 4.3198 || ValLoss: 4.2083 || Time: 0.407374\n",
      "Iteration 20  || LR 0.000191 || TrainLoss: 2.6081 || ValLoss: 2.4505 || Time: 0.417677\n",
      "Iteration 30  || LR 0.000291 || TrainLoss: 1.8270 || ValLoss: 1.7621 || Time: 0.407475\n",
      "Iteration 40  || LR 0.000391 || TrainLoss: 1.2916 || ValLoss: 1.0827 || Time: 0.416326\n",
      "Iteration 50  || LR 0.000491 || TrainLoss: 0.8419 || ValLoss: 0.9368 || Time: 0.405837\n",
      "Iteration 60  || LR 0.000590 || TrainLoss: 0.5097 || ValLoss: 0.6325 || Time: 0.406980\n",
      "Iteration 70  || LR 0.000690 || TrainLoss: 0.6444 || ValLoss: 0.6766 || Time: 0.439231\n",
      "Iteration 80  || LR 0.000790 || TrainLoss: 0.5835 || ValLoss: 0.7020 || Time: 0.436677\n",
      "Iteration 90  || LR 0.000890 || TrainLoss: 0.7994 || ValLoss: 1.1169 || Time: 0.418029\n",
      "Iteration 100  || LR 0.000990 || TrainLoss: 1.6877 || ValLoss: 0.7214 || Time: 0.438395\n",
      "Iteration 110  || LR 0.001000 || TrainLoss: 0.5955 || ValLoss: 1.0032 || Time: 0.412695\n",
      "Iteration 120  || LR 0.001000 || TrainLoss: 0.4292 || ValLoss: 0.6477 || Time: 0.408261\n",
      "Iteration 130  || LR 0.001000 || TrainLoss: 0.5531 || ValLoss: 0.6239 || Time: 0.412038\n",
      "Iteration 140  || LR 0.001000 || TrainLoss: 0.3446 || ValLoss: 0.8076 || Time: 0.405869\n",
      "Iteration 150  || LR 0.001000 || TrainLoss: 0.4967 || ValLoss: 0.6161 || Time: 0.417000\n",
      "Iteration 160  || LR 0.001000 || TrainLoss: 0.4616 || ValLoss: 0.6250 || Time: 0.424391\n",
      "Iteration 170  || LR 0.001000 || TrainLoss: 0.4358 || ValLoss: 0.5863 || Time: 0.439590\n",
      "Iteration 180  || LR 0.001000 || TrainLoss: 0.3337 || ValLoss: 0.5720 || Time: 0.408154\n",
      "Iteration 190  || LR 0.001000 || TrainLoss: 0.3934 || ValLoss: 0.5766 || Time: 0.394654\n",
      "Iteration 200  || LR 0.001000 || TrainLoss: 0.3541 || ValLoss: 0.5812 || Time: 0.418752\n",
      "Iteration 210  || LR 0.001000 || TrainLoss: 0.5253 || ValLoss: 0.5427 || Time: 0.418199\n",
      "Iteration 220  || LR 0.001000 || TrainLoss: 0.3268 || ValLoss: 0.5357 || Time: 0.403322\n",
      "Iteration 230  || LR 0.001000 || TrainLoss: 0.4087 || ValLoss: 0.5514 || Time: 0.412616\n",
      "Iteration 240  || LR 0.001000 || TrainLoss: 0.4039 || ValLoss: 0.5573 || Time: 0.405020\n",
      "Iteration 250  || LR 0.001000 || TrainLoss: 0.4079 || ValLoss: 0.5482 || Time: 0.397208\n",
      "Iteration 260  || LR 0.001000 || TrainLoss: 0.3794 || ValLoss: 0.4550 || Time: 0.411029\n",
      "Iteration 270  || LR 0.001000 || TrainLoss: 0.4104 || ValLoss: 0.4235 || Time: 0.416323\n",
      "Iteration 280  || LR 0.001000 || TrainLoss: 0.3565 || ValLoss: 0.4356 || Time: 0.403017\n",
      "Iteration 290  || LR 0.001000 || TrainLoss: 0.4097 || ValLoss: 0.4197 || Time: 0.427994\n",
      "Iteration 300  || LR 0.001000 || TrainLoss: 0.2977 || ValLoss: 0.3973 || Time: 0.410565\n",
      "Iteration 310  || LR 0.001000 || TrainLoss: 0.3243 || ValLoss: 0.3990 || Time: 0.431158\n",
      "Iteration 320  || LR 0.001000 || TrainLoss: 0.3484 || ValLoss: 0.3722 || Time: 0.410962\n",
      "Iteration 330  || LR 0.001000 || TrainLoss: 0.2694 || ValLoss: 0.4012 || Time: 0.401059\n",
      "Iteration 340  || LR 0.001000 || TrainLoss: 0.3396 || ValLoss: 0.4197 || Time: 0.413652\n",
      "Iteration 350  || LR 0.001000 || TrainLoss: 0.3099 || ValLoss: 0.4392 || Time: 0.398732\n",
      "Iteration 360  || LR 0.001000 || TrainLoss: 0.3611 || ValLoss: 0.3578 || Time: 0.427651\n",
      "Iteration 370  || LR 0.001000 || TrainLoss: 0.3204 || ValLoss: 0.4717 || Time: 0.430071\n",
      "Iteration 380  || LR 0.001000 || TrainLoss: 0.8398 || ValLoss: 0.3815 || Time: 0.413991\n",
      "Iteration 390  || LR 0.001000 || TrainLoss: 1.6693 || ValLoss: 0.4835 || Time: 0.412559\n",
      "Iteration 400  || LR 0.001000 || TrainLoss: 0.2972 || ValLoss: 0.4510 || Time: 0.409408\n",
      "Iteration 410  || LR 0.001000 || TrainLoss: 0.4984 || ValLoss: 0.3491 || Time: 0.400859\n",
      "Iteration 420  || LR 0.001000 || TrainLoss: 0.2668 || ValLoss: 0.4265 || Time: 0.401138\n",
      "Iteration 430  || LR 0.001000 || TrainLoss: 0.2721 || ValLoss: 0.3150 || Time: 0.421415\n",
      "Iteration 440  || LR 0.001000 || TrainLoss: 0.3144 || ValLoss: 0.3668 || Time: 0.423762\n",
      "Iteration 450  || LR 0.001000 || TrainLoss: 0.5793 || ValLoss: 0.3336 || Time: 0.408041\n",
      "Iteration 460  || LR 0.001000 || TrainLoss: 0.3555 || ValLoss: 0.4461 || Time: 0.436106\n",
      "Iteration 470  || LR 0.001000 || TrainLoss: 0.4809 || ValLoss: 0.2733 || Time: 0.435366\n",
      "Iteration 480  || LR 0.001000 || TrainLoss: 0.3223 || ValLoss: 0.2665 || Time: 0.413062\n",
      "Iteration 490  || LR 0.001000 || TrainLoss: 0.3120 || ValLoss: 0.2565 || Time: 0.423703\n",
      "Iteration 500  || LR 0.001000 || TrainLoss: 0.2512 || ValLoss: 0.4190 || Time: 0.446827\n",
      "Iteration 510  || LR 0.001000 || TrainLoss: 0.2759 || ValLoss: 0.2459 || Time: 0.410602\n",
      "Iteration 520  || LR 0.001000 || TrainLoss: 0.1600 || ValLoss: 0.2262 || Time: 0.401359\n",
      "Iteration 530  || LR 0.001000 || TrainLoss: 0.2616 || ValLoss: 0.1859 || Time: 0.411807\n",
      "Iteration 540  || LR 0.001000 || TrainLoss: 0.3257 || ValLoss: 0.1948 || Time: 0.428074\n",
      "Iteration 550  || LR 0.001000 || TrainLoss: 0.1843 || ValLoss: 0.1843 || Time: 0.418681\n",
      "Iteration 560  || LR 0.001000 || TrainLoss: 0.1557 || ValLoss: 0.1667 || Time: 0.431169\n",
      "Iteration 570  || LR 0.001000 || TrainLoss: 0.1887 || ValLoss: 0.1459 || Time: 0.411887\n",
      "Iteration 580  || LR 0.001000 || TrainLoss: 0.1487 || ValLoss: 0.1778 || Time: 0.399692\n",
      "Iteration 590  || LR 0.001000 || TrainLoss: 0.2641 || ValLoss: 0.1451 || Time: 0.429087\n",
      "Iteration 600  || LR 0.001000 || TrainLoss: 0.5485 || ValLoss: 0.5958 || Time: 0.432760\n",
      "Iteration 610  || LR 0.001000 || TrainLoss: 0.2671 || ValLoss: 0.4214 || Time: 0.439759\n",
      "Iteration 620  || LR 0.001000 || TrainLoss: 0.2150 || ValLoss: 0.3122 || Time: 0.397342\n",
      "Iteration 630  || LR 0.001000 || TrainLoss: 0.2163 || ValLoss: 0.2894 || Time: 0.400362\n",
      "Iteration 640  || LR 0.001000 || TrainLoss: 0.1650 || ValLoss: 0.4499 || Time: 0.401104\n",
      "Iteration 650  || LR 0.001000 || TrainLoss: 0.3128 || ValLoss: 0.3581 || Time: 0.405402\n",
      "Iteration 660  || LR 0.001000 || TrainLoss: 0.2751 || ValLoss: 0.2639 || Time: 0.426814\n",
      "Iteration 670  || LR 0.001000 || TrainLoss: 0.2116 || ValLoss: 0.1435 || Time: 0.425972\n",
      "Iteration 680  || LR 0.001000 || TrainLoss: 0.0948 || ValLoss: 0.1342 || Time: 0.429361\n",
      "Iteration 690  || LR 0.001000 || TrainLoss: 0.1510 || ValLoss: 0.2486 || Time: 0.414948\n",
      "Iteration 700  || LR 0.001000 || TrainLoss: 0.3993 || ValLoss: 0.4171 || Time: 0.417547\n",
      "Iteration 710  || LR 0.001000 || TrainLoss: 0.1816 || ValLoss: 0.4962 || Time: 0.439758\n",
      "Iteration 720  || LR 0.001000 || TrainLoss: 0.2491 || ValLoss: 0.2499 || Time: 0.412901\n",
      "Iteration 730  || LR 0.001000 || TrainLoss: 0.2422 || ValLoss: 0.2537 || Time: 0.425579\n",
      "Iteration 740  || LR 0.001000 || TrainLoss: 0.1037 || ValLoss: 0.1297 || Time: 0.403926\n",
      "Iteration 750  || LR 0.001000 || TrainLoss: 0.1195 || ValLoss: 0.1202 || Time: 0.478345\n",
      "Iteration 760  || LR 0.001000 || TrainLoss: 0.0816 || ValLoss: 0.1287 || Time: 0.402910\n",
      "Iteration 770  || LR 0.001000 || TrainLoss: 0.0891 || ValLoss: 0.1063 || Time: 0.403935\n",
      "Iteration 780  || LR 0.001000 || TrainLoss: 0.0914 || ValLoss: 0.0986 || Time: 0.408628\n",
      "Iteration 790  || LR 0.001000 || TrainLoss: 0.0823 || ValLoss: 0.0895 || Time: 0.412603\n",
      "Iteration 800  || LR 0.001000 || TrainLoss: 0.1262 || ValLoss: 0.2908 || Time: 0.410617\n",
      "Iteration 810  || LR 0.001000 || TrainLoss: 0.1843 || ValLoss: 0.1139 || Time: 0.412043\n",
      "Iteration 820  || LR 0.001000 || TrainLoss: 0.1702 || ValLoss: 0.2182 || Time: 0.409603\n",
      "Iteration 830  || LR 0.001000 || TrainLoss: 0.1904 || ValLoss: 0.1164 || Time: 0.419823\n",
      "Iteration 840  || LR 0.001000 || TrainLoss: 0.1050 || ValLoss: 0.1007 || Time: 0.442767\n",
      "Iteration 850  || LR 0.001000 || TrainLoss: 0.0955 || ValLoss: 0.1309 || Time: 0.403500\n",
      "Iteration 860  || LR 0.001000 || TrainLoss: 0.0773 || ValLoss: 0.0875 || Time: 0.425633\n",
      "Iteration 870  || LR 0.001000 || TrainLoss: 0.0773 || ValLoss: 0.1193 || Time: 0.423881\n",
      "Iteration 880  || LR 0.001000 || TrainLoss: 0.2578 || ValLoss: 0.1219 || Time: 0.406303\n",
      "Iteration 890  || LR 0.001000 || TrainLoss: 0.1935 || ValLoss: 0.1810 || Time: 0.430573\n",
      "Iteration 900  || LR 0.001000 || TrainLoss: 0.0874 || ValLoss: 0.2491 || Time: 0.413726\n",
      "Iteration 910  || LR 0.001000 || TrainLoss: 0.1352 || ValLoss: 0.1788 || Time: 0.411541\n",
      "Iteration 920  || LR 0.001000 || TrainLoss: 0.1930 || ValLoss: 0.1875 || Time: 0.424610\n",
      "Iteration 930  || LR 0.001000 || TrainLoss: 0.1019 || ValLoss: 0.1737 || Time: 0.405659\n",
      "Iteration 940  || LR 0.001000 || TrainLoss: 0.1689 || ValLoss: 0.1475 || Time: 0.416156\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 950  || LR 0.001000 || TrainLoss: 0.1258 || ValLoss: 0.1874 || Time: 0.405660\n",
      "Iteration 960  || LR 0.001000 || TrainLoss: 0.0820 || ValLoss: 0.1757 || Time: 0.428074\n",
      "Iteration 970  || LR 0.001000 || TrainLoss: 0.2705 || ValLoss: 0.1807 || Time: 0.415206\n",
      "Iteration 980  || LR 0.001000 || TrainLoss: 0.0972 || ValLoss: 0.1970 || Time: 0.414114\n",
      "Iteration 990  || LR 0.001000 || TrainLoss: 0.0877 || ValLoss: 0.1436 || Time: 0.419866\n",
      "Iteration 1000  || LR 0.001000 || TrainLoss: 0.1157 || ValLoss: 0.2091 || Time: 0.424750\n",
      "Iteration 1010  || LR 0.000100 || TrainLoss: 0.0943 || ValLoss: 0.0856 || Time: 0.410094\n",
      "Iteration 1020  || LR 0.000100 || TrainLoss: 0.0790 || ValLoss: 0.0983 || Time: 0.434888\n",
      "Iteration 1030  || LR 0.000100 || TrainLoss: 0.0573 || ValLoss: 0.0835 || Time: 0.442300\n",
      "Iteration 1040  || LR 0.000100 || TrainLoss: 0.0642 || ValLoss: 0.0717 || Time: 0.417977\n",
      "Iteration 1050  || LR 0.000100 || TrainLoss: 0.0561 || ValLoss: 0.0724 || Time: 0.417742\n",
      "Iteration 1060  || LR 0.000100 || TrainLoss: 0.0555 || ValLoss: 0.0709 || Time: 0.423152\n",
      "Iteration 1070  || LR 0.000100 || TrainLoss: 0.0593 || ValLoss: 0.0727 || Time: 0.441209\n",
      "Iteration 1080  || LR 0.000100 || TrainLoss: 0.0513 || ValLoss: 0.0720 || Time: 0.425723\n",
      "Iteration 1090  || LR 0.000100 || TrainLoss: 0.0505 || ValLoss: 0.0713 || Time: 0.400737\n",
      "Iteration 1100  || LR 0.000100 || TrainLoss: 0.0545 || ValLoss: 0.0699 || Time: 0.411767\n",
      "Iteration 1110  || LR 0.000100 || TrainLoss: 0.0549 || ValLoss: 0.0683 || Time: 0.417730\n",
      "Iteration 1120  || LR 0.000100 || TrainLoss: 0.0546 || ValLoss: 0.0695 || Time: 0.405880\n",
      "Iteration 1130  || LR 0.000100 || TrainLoss: 0.0467 || ValLoss: 0.0703 || Time: 0.417414\n",
      "Iteration 1140  || LR 0.000100 || TrainLoss: 0.0428 || ValLoss: 0.0684 || Time: 0.390442\n",
      "Iteration 1150  || LR 0.000100 || TrainLoss: 0.0473 || ValLoss: 0.0678 || Time: 0.404936\n",
      "Iteration 1160  || LR 0.000100 || TrainLoss: 0.0439 || ValLoss: 0.0658 || Time: 0.402166\n",
      "Iteration 1170  || LR 0.000100 || TrainLoss: 0.0495 || ValLoss: 0.0665 || Time: 0.411578\n",
      "Iteration 1180  || LR 0.000100 || TrainLoss: 0.0498 || ValLoss: 0.0692 || Time: 0.397382\n",
      "Iteration 1190  || LR 0.000100 || TrainLoss: 0.0496 || ValLoss: 0.0681 || Time: 0.404322\n",
      "Iteration 1200  || LR 0.000100 || TrainLoss: 0.0530 || ValLoss: 0.0689 || Time: 0.426564\n",
      "Iteration 1210  || LR 0.000100 || TrainLoss: 0.0509 || ValLoss: 0.0687 || Time: 0.410039\n",
      "Iteration 1220  || LR 0.000100 || TrainLoss: 0.0543 || ValLoss: 0.0682 || Time: 0.400486\n",
      "Iteration 1230  || LR 0.000100 || TrainLoss: 0.0491 || ValLoss: 0.0697 || Time: 0.416936\n",
      "Iteration 1240  || LR 0.000100 || TrainLoss: 0.0588 || ValLoss: 0.0713 || Time: 0.403025\n",
      "Iteration 1250  || LR 0.000100 || TrainLoss: 0.0510 || ValLoss: 0.0678 || Time: 0.409647\n",
      "Iteration 1260  || LR 0.000100 || TrainLoss: 0.0465 || ValLoss: 0.0664 || Time: 0.424788\n",
      "Iteration 1270  || LR 0.000100 || TrainLoss: 0.0528 || ValLoss: 0.0675 || Time: 0.436040\n",
      "Iteration 1280  || LR 0.000100 || TrainLoss: 0.0616 || ValLoss: 0.0660 || Time: 0.418906\n",
      "Iteration 1290  || LR 0.000100 || TrainLoss: 0.0471 || ValLoss: 0.0662 || Time: 0.395830\n",
      "Iteration 1300  || LR 0.000100 || TrainLoss: 0.0506 || ValLoss: 0.0669 || Time: 0.409673\n",
      "Iteration 1310  || LR 0.000100 || TrainLoss: 0.0503 || ValLoss: 0.0665 || Time: 0.411894\n",
      "Iteration 1320  || LR 0.000100 || TrainLoss: 0.0511 || ValLoss: 0.0673 || Time: 0.416854\n",
      "Iteration 1330  || LR 0.000100 || TrainLoss: 0.0510 || ValLoss: 0.0661 || Time: 0.427692\n",
      "Iteration 1340  || LR 0.000100 || TrainLoss: 0.0517 || ValLoss: 0.0663 || Time: 0.428933\n",
      "Iteration 1350  || LR 0.000100 || TrainLoss: 0.0426 || ValLoss: 0.0647 || Time: 0.414245\n",
      "Iteration 1360  || LR 0.000100 || TrainLoss: 0.0495 || ValLoss: 0.0672 || Time: 0.416826\n",
      "Iteration 1370  || LR 0.000100 || TrainLoss: 0.0412 || ValLoss: 0.0653 || Time: 0.428884\n",
      "Iteration 1380  || LR 0.000100 || TrainLoss: 0.0423 || ValLoss: 0.0627 || Time: 0.403820\n",
      "Iteration 1390  || LR 0.000100 || TrainLoss: 0.0425 || ValLoss: 0.0656 || Time: 0.416624\n",
      "Iteration 1400  || LR 0.000100 || TrainLoss: 0.0501 || ValLoss: 0.0676 || Time: 0.413606\n",
      "Iteration 1410  || LR 0.000100 || TrainLoss: 0.0549 || ValLoss: 0.0636 || Time: 0.417778\n",
      "Iteration 1420  || LR 0.000100 || TrainLoss: 0.0455 || ValLoss: 0.0639 || Time: 0.405343\n",
      "Iteration 1430  || LR 0.000100 || TrainLoss: 0.0488 || ValLoss: 0.0641 || Time: 0.416901\n",
      "Iteration 1440  || LR 0.000100 || TrainLoss: 0.0508 || ValLoss: 0.0675 || Time: 0.402364\n",
      "Iteration 1450  || LR 0.000100 || TrainLoss: 0.0449 || ValLoss: 0.0625 || Time: 0.402654\n",
      "Iteration 1460  || LR 0.000100 || TrainLoss: 0.0433 || ValLoss: 0.0638 || Time: 0.411039\n",
      "Iteration 1470  || LR 0.000100 || TrainLoss: 0.0414 || ValLoss: 0.0641 || Time: 0.404287\n",
      "Iteration 1480  || LR 0.000100 || TrainLoss: 0.0559 || ValLoss: 0.0649 || Time: 0.442156\n",
      "Iteration 1490  || LR 0.000100 || TrainLoss: 0.0487 || ValLoss: 0.0631 || Time: 0.425087\n",
      "Iteration 1500  || LR 0.000100 || TrainLoss: 0.0425 || ValLoss: 0.0626 || Time: 0.428533\n",
      "Iteration 1510  || LR 0.000100 || TrainLoss: 0.0428 || ValLoss: 0.0645 || Time: 0.411997\n",
      "Iteration 1520  || LR 0.000100 || TrainLoss: 0.0440 || ValLoss: 0.0619 || Time: 0.419404\n",
      "Iteration 1530  || LR 0.000100 || TrainLoss: 0.0499 || ValLoss: 0.0644 || Time: 0.427943\n",
      "Iteration 1540  || LR 0.000100 || TrainLoss: 0.0530 || ValLoss: 0.0619 || Time: 0.413872\n",
      "Iteration 1550  || LR 0.000100 || TrainLoss: 0.0418 || ValLoss: 0.0629 || Time: 0.411679\n",
      "Iteration 1560  || LR 0.000100 || TrainLoss: 0.0391 || ValLoss: 0.0626 || Time: 0.403920\n",
      "Iteration 1570  || LR 0.000100 || TrainLoss: 0.0407 || ValLoss: 0.0621 || Time: 0.427264\n",
      "Iteration 1580  || LR 0.000100 || TrainLoss: 0.0500 || ValLoss: 0.0642 || Time: 0.435461\n",
      "Iteration 1590  || LR 0.000100 || TrainLoss: 0.0442 || ValLoss: 0.0640 || Time: 0.408146\n",
      "Iteration 1600  || LR 0.000100 || TrainLoss: 0.0445 || ValLoss: 0.0619 || Time: 0.423789\n",
      "Iteration 1610  || LR 0.000010 || TrainLoss: 0.0549 || ValLoss: 0.0625 || Time: 0.399291\n",
      "Iteration 1620  || LR 0.000010 || TrainLoss: 0.0399 || ValLoss: 0.0624 || Time: 0.410384\n",
      "Iteration 1630  || LR 0.000010 || TrainLoss: 0.0400 || ValLoss: 0.0620 || Time: 0.417293\n",
      "Iteration 1640  || LR 0.000010 || TrainLoss: 0.0398 || ValLoss: 0.0619 || Time: 0.412076\n",
      "Iteration 1650  || LR 0.000010 || TrainLoss: 0.0421 || ValLoss: 0.0630 || Time: 0.428933\n",
      "Iteration 1660  || LR 0.000010 || TrainLoss: 0.0359 || ValLoss: 0.0626 || Time: 0.424552\n",
      "Iteration 1670  || LR 0.000010 || TrainLoss: 0.0444 || ValLoss: 0.0624 || Time: 0.416027\n",
      "Iteration 1680  || LR 0.000010 || TrainLoss: 0.0431 || ValLoss: 0.0624 || Time: 0.410083\n",
      "Iteration 1690  || LR 0.000010 || TrainLoss: 0.0436 || ValLoss: 0.0622 || Time: 0.399355\n",
      "Iteration 1700  || LR 0.000010 || TrainLoss: 0.0371 || ValLoss: 0.0619 || Time: 0.429681\n",
      "Iteration 1710  || LR 0.000010 || TrainLoss: 0.0377 || ValLoss: 0.0619 || Time: 0.413879\n",
      "Iteration 1720  || LR 0.000010 || TrainLoss: 0.0465 || ValLoss: 0.0624 || Time: 0.410352\n",
      "Iteration 1730  || LR 0.000010 || TrainLoss: 0.0436 || ValLoss: 0.0615 || Time: 0.410664\n",
      "Iteration 1740  || LR 0.000010 || TrainLoss: 0.0423 || ValLoss: 0.0618 || Time: 0.409529\n",
      "Iteration 1750  || LR 0.000010 || TrainLoss: 0.0458 || ValLoss: 0.0623 || Time: 0.401319\n",
      "Iteration 1760  || LR 0.000010 || TrainLoss: 0.0395 || ValLoss: 0.0621 || Time: 0.413955\n",
      "Iteration 1770  || LR 0.000010 || TrainLoss: 0.0488 || ValLoss: 0.0619 || Time: 0.413626\n",
      "Iteration 1780  || LR 0.000010 || TrainLoss: 0.0377 || ValLoss: 0.0619 || Time: 0.419307\n",
      "Iteration 1790  || LR 0.000010 || TrainLoss: 0.0392 || ValLoss: 0.0618 || Time: 0.399504\n",
      "Iteration 1800  || LR 0.000010 || TrainLoss: 0.0442 || ValLoss: 0.0617 || Time: 0.418180\n",
      "Iteration 1810  || LR 0.000010 || TrainLoss: 0.0438 || ValLoss: 0.0623 || Time: 0.420916\n",
      "Iteration 1820  || LR 0.000010 || TrainLoss: 0.0429 || ValLoss: 0.0620 || Time: 0.428664\n",
      "Iteration 1830  || LR 0.000010 || TrainLoss: 0.0402 || ValLoss: 0.0624 || Time: 0.418600\n",
      "Iteration 1840  || LR 0.000010 || TrainLoss: 0.0449 || ValLoss: 0.0619 || Time: 0.400931\n",
      "Iteration 1850  || LR 0.000010 || TrainLoss: 0.0384 || ValLoss: 0.0612 || Time: 0.422927\n",
      "Iteration 1860  || LR 0.000010 || TrainLoss: 0.0475 || ValLoss: 0.0610 || Time: 0.445792\n",
      "Iteration 1870  || LR 0.000010 || TrainLoss: 0.0393 || ValLoss: 0.0612 || Time: 0.421203\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1880  || LR 0.000010 || TrainLoss: 0.0467 || ValLoss: 0.0615 || Time: 0.436267\n",
      "Iteration 1890  || LR 0.000010 || TrainLoss: 0.0426 || ValLoss: 0.0618 || Time: 0.411060\n",
      "Iteration 1900  || LR 0.000010 || TrainLoss: 0.0479 || ValLoss: 0.0618 || Time: 0.417695\n",
      "Iteration 1910  || LR 0.000010 || TrainLoss: 0.0492 || ValLoss: 0.0615 || Time: 0.417094\n",
      "Iteration 1920  || LR 0.000010 || TrainLoss: 0.0436 || ValLoss: 0.0615 || Time: 0.419155\n",
      "Iteration 1930  || LR 0.000010 || TrainLoss: 0.0412 || ValLoss: 0.0618 || Time: 0.404781\n",
      "Iteration 1940  || LR 0.000010 || TrainLoss: 0.0399 || ValLoss: 0.0615 || Time: 0.448470\n",
      "Iteration 1950  || LR 0.000010 || TrainLoss: 0.0361 || ValLoss: 0.0614 || Time: 0.398841\n",
      "Iteration 1960  || LR 0.000010 || TrainLoss: 0.0365 || ValLoss: 0.0609 || Time: 0.435040\n",
      "Iteration 1970  || LR 0.000010 || TrainLoss: 0.0464 || ValLoss: 0.0617 || Time: 0.427393\n",
      "Iteration 1980  || LR 0.000010 || TrainLoss: 0.0550 || ValLoss: 0.0628 || Time: 0.427492\n",
      "Iteration 1990  || LR 0.000010 || TrainLoss: 0.0490 || ValLoss: 0.0613 || Time: 0.410609\n",
      "Iteration 2000  || LR 0.000010 || TrainLoss: 0.0402 || ValLoss: 0.0616 || Time: 0.415814\n",
      "Finish Training...\n"
     ]
    }
   ],
   "source": [
    "lr_decay = [1000,1600]\n",
    "step_index = 0\n",
    "tlall = []\n",
    "vlall = []\n",
    "for iteration in range(2000):\n",
    "    running_loss= 0\n",
    "    val_loss = 0\n",
    "    start = time.time()\n",
    "    batch_iterator = iter(trainloader)\n",
    "    images, labels = next(batch_iterator)\n",
    "#     print(images)\n",
    "    images = images.to(device)\n",
    "    labels = labels.to(device)\n",
    "    val_iter = iter(valloader)\n",
    "    valimg,vallabels = next(val_iter)\n",
    "    valimg = valimg.to(device)\n",
    "    vallabels = vallabels.to(device)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    outputs= net(images)\n",
    "#     print(outputs.size())\n",
    "#     print(labels.size())\n",
    "    outputs = outputs.squeeze(2)\n",
    "#     print(outputs.size())\n",
    "    loss = criterion(outputs,labels)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    running_loss += loss.item()\n",
    "    \n",
    "    val_out = net(valimg)\n",
    "    val_out = val_out.squeeze(2)\n",
    "    valloss = criterion(val_out,vallabels)\n",
    "    val_loss += valloss.item()\n",
    "    \n",
    "    \n",
    "    if iteration <=100:\n",
    "        warmup_learning_rate(optimizer,iteration)\n",
    "    if iteration in lr_decay:\n",
    "        step_index += 1\n",
    "        adjust_learning_rate(optimizer, 0.1, step_index)\n",
    "    for param in optimizer.param_groups:\n",
    "        if 'lr' in param.keys():  \n",
    "            if iteration % 10 == 9:\n",
    "                tlall.append(running_loss / 10)\n",
    "                vlall.append(val_loss / 10)\n",
    "                print(\"Iteration %d  || LR %f || TrainLoss: %.4f || ValLoss: %.4f || Time: %f\"%(iteration+1,param['lr'],running_loss / 10,val_loss / 10,time.time()-start))\n",
    "    running_loss = 0\n",
    "            \n",
    "print(\"Finish Training...\")\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net.state_dict(), \"vggregnet.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(range(len(tlall)))\n",
    "print(x)\n",
    "plt.plot(x,tlall)\n",
    "plt.plot(x,vlall)\n",
    "plt.title('Train&Val Loss')\n",
    "plt.xlabel('iterations')\n",
    "plt.ylabel('loss')\n",
    " \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单张图片测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f34a0a147d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img1 = io.imread(\"./PlateKeyPoint/val/20171110083856993_粤B02405D_sp.jpg\")\n",
    "\n",
    "img1 = transform.resize(img1, (64, 128))\n",
    "plt.imshow(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = img1.transpose((2, 0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.array(img)\n",
    "img = torch.from_numpy(img)\n",
    "img = img.unsqueeze(0)\n",
    "img = img.type(torch.FloatTensor)\n",
    "img = img.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = net(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = predict.squeeze(2)\n",
    "predict = predict.squeeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkp = predict.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f34a097f150>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img1)\n",
    "plt.scatter(pkp[:, 0], pkp[:, 1], s=10, marker='.', c='r',linewidths = 5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 验证集批量测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 torch.Size([3, 64, 128]) torch.Size([16, 2])\n",
      "torch.Size([3, 64, 128])\n",
      "1 torch.Size([3, 64, 128]) torch.Size([16, 2])\n",
      "torch.Size([3, 64, 128])\n",
      "2 torch.Size([3, 64, 128]) torch.Size([16, 2])\n",
      "torch.Size([3, 64, 128])\n",
      "3 torch.Size([3, 64, 128]) torch.Size([16, 2])\n",
      "torch.Size([3, 64, 128])\n",
      "4 torch.Size([3, 64, 128]) torch.Size([16, 2])\n",
      "torch.Size([3, 64, 128])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x7200 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,100))\n",
    "for i in range(len(platekpval)):\n",
    "    sample = platekpval[i]\n",
    "    print(i, sample[0].shape, sample[1].shape )\n",
    "    predict = net(sample[0].unsqueeze(0).cuda())\n",
    "    predict = predict.squeeze(2)\n",
    "    predict = predict.squeeze(0)\n",
    "    pkp = predict.cpu().detach().numpy()\n",
    "    ax = plt.subplot(1,5,i+1)\n",
    "    plt.tight_layout()\n",
    "    ax.set_title('keypoint #{}'.format(i))\n",
    "    ax.axis('off')\n",
    "    print(sample[0].size())\n",
    "    plt.imshow(sample[0].cpu().numpy().transpose((1, 2, 0)))\n",
    "    plt.scatter(pkp[:, 0], pkp[:, 1], s=10, marker='.', c='r',linewidths = 5) \n",
    "#     print(*sample.values())\n",
    "\n",
    "    if i == 4:\n",
    "        plt.show()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
